State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

Jinhao Meng; Guangzhao Luo; Fei Gao

doi:10.1109/ITEC-AP.2014.6940680

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

Jinhao Meng
, Guangzhao Luo
, Fei Gao

自动化学院

Northwestern Polytechnical University Xian
Université de technologie de Belfort Montbéliard

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

14 引用（Scopus）

摘要

A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.

源语言	英语
主期刊名	IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings
出版商	Institute of Electrical and Electronics Engineers Inc.
ISBN（电子版）	9781479942398
DOI	https://doi.org/10.1109/ITEC-AP.2014.6940680
出版状态	已出版 - 30 10月 2014
活动	2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Beijing, 中国期限: 31 8月 2014 → 3 9月 2014

出版系列

姓名	IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings

会议

会议	2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014
国家/地区	中国
市	Beijing
时期	31/08/14 → 3/09/14

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.1109/ITEC-AP.2014.6940680

其它文件与链接

链接到 Scopus 的出版物

引用此

Meng, J., Luo, G., & Gao, F. (2014). State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. 在 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings 文章 6940680 (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITEC-AP.2014.6940680

Meng, Jinhao ; Luo, Guangzhao ; Gao, Fei. / State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings).

@inproceedings{a36a697ea3804b33b9908e5895f32163,

title = "State-of-charge estimation for lithium-ion battery using AUKF and LSSVM",

abstract = "A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.",

keywords = "adaptive unscented Kalman filter (AUKF), Battery, least squares support vector machine (LSSVM), state of charge (SOC)",

author = "Jinhao Meng and Guangzhao Luo and Fei Gao",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 ; Conference date: 31-08-2014 Through 03-09-2014",

year = "2014",

month = oct,

day = "30",

doi = "10.1109/ITEC-AP.2014.6940680",

language = "英语",

series = "IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings",

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booktitle = "IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings",

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Meng, J, Luo, G & Gao, F 2014, State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. 在 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings., 6940680, IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014, Beijing, 中国, 31/08/14. https://doi.org/10.1109/ITEC-AP.2014.6940680

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. / Meng, Jinhao; Luo, Guangzhao; Gao, Fei.
IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. 6940680 (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

AU - Meng, Jinhao

AU - Luo, Guangzhao

AU - Gao, Fei

PY - 2014/10/30

Y1 - 2014/10/30

N2 - A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.

AB - A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.

KW - adaptive unscented Kalman filter (AUKF)

KW - Battery

KW - least squares support vector machine (LSSVM)

KW - state of charge (SOC)

UR - https://www.scopus.com/pages/publications/84916235076

U2 - 10.1109/ITEC-AP.2014.6940680

DO - 10.1109/ITEC-AP.2014.6940680

M3 - 会议稿件

AN - SCOPUS:84916235076

T3 - IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings

BT - IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014

Y2 - 31 August 2014 through 3 September 2014

ER -

Meng J, Luo G, Gao F. State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. 在 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. 6940680. (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings). doi: 10.1109/ITEC-AP.2014.6940680

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此